A Safer Approach to Build Recommendation Systems on Unidentifiable Data

Kishor Gupta, Akib Sadmanee, Nafiz Sadman

2022

Abstract

In recent years, data security has been one of the biggest concerns, and individuals have grown increasingly worried about the security of their personal information. Personalization typically necessitates the collection of individual data for analysis, exposing customers to privacy concerns. Companies create an illusion of safety to make people feel safe using a mainstream word, “encryption”. Though encryption protects personal data from an external breach, the companies can still exploit personal data collected from users as they own the encryption keys. We present a naive yet secure approach for recommending movies to consumers without collecting any personally identifiable information. Our proposed approach can assist a movie recommendation system understand user preferences using the user’s movie watch-time and watch history only. We conducted a comprehensive and comparative study on the performance of three deep reinforcement learning architectures, namely DQN, DDQN, and D3QN, on the same task. We observed that D3QN outperformed the other two architectures and achieved a precision of 0.880, recall of 0.805, and F1 score of 0.830. The results show that we can build a competitive movie recommendation system using unidentifiable data.

Paper Citation

in Harvard Style

Gupta K., Sadmanee A. and Sadman N. (2022). A Safer Approach to Build Recommendation Systems on Unidentifiable Data. In Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART, ISBN 978-989-758-547-0, pages 589-596. DOI: 10.5220/0010868000003116

in Bibtex Style

@conference{icaart22,
author={Kishor Gupta and Akib Sadmanee and Nafiz Sadman},
title={A Safer Approach to Build Recommendation Systems on Unidentifiable Data},
booktitle={Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART,},
year={2022},
pages={589-596},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010868000003116},
isbn={978-989-758-547-0},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART,
TI - A Safer Approach to Build Recommendation Systems on Unidentifiable Data
SN - 978-989-758-547-0
AU - Gupta K.
AU - Sadmanee A.
AU - Sadman N.
PY - 2022
SP - 589
EP - 596
DO - 10.5220/0010868000003116